An Algorithm to Characterize a Dementia Population by Disease Subtype. (April 2019)

• Record Type: Journal Article
• Authors: Albrecht, Jennifer S.; Hanna, Maya; Randall, Rhonda L.; Kim, Dure; Perfetto, Eleanor M.
• Is Part Of: Alzheimer disease and associated disorders. Volume 33:Number 2(2019)
• Publication Date: 2019-04
• Subjects: Alzheimer disease -- dementia subtype -- algorithm -- administrative claims data -- characterization; Alzheimer's disease -- Periodicals; Dementia -- Periodicals; 616.8305
• DOI: 10.1097/WAD.0000000000000295
• Languages: English
• ISSNs: 0893-0341

Abstract

Abstract : Purpose: Identification of Alzheimer disease and related dementias (ADRD) subtypes is important for pharmacologic treatment and care planning, yet inaccuracies in dementia diagnoses make ADRD subtypes hard to identify and characterize. The objectives of this study were to (1) develop a method to categorize ADRD cases by subtype and (2) characterize and compare the ADRD subtype populations by demographic and other characteristics. Methods: We identified cases of ADRD occurring during 2008 to 2014 from the OptumLabs Database using diagnosis codes and antidementia medication fills. We developed a categorization algorithm that made use of temporal sequencing of diagnoses and provider type. Results: We identified 36, 838 individuals with ADRD. After application of our algorithm, the largest proportion of cases were nonspecific dementia (41.2%), followed by individuals with antidementia medication but no ADRD diagnosis (15.6%). Individuals with Alzheimer disease formed 10.2% of cases. Individuals with vascular dementia had the greatest burden of comorbid disease. Initial documentation of dementia occurred primarily in the office setting (35.1%). Discussion: Our algorithm identified 6 dementia subtypes and three additional categories representing unique diagnostic patterns in the data. Differences and similarities between groups provided support for the approach and offered unique insight into ADRD subtype characteristics.